SNB-PPB: Social-network-based-privacy-preserving Broadcast for
Vehicular Communications
Yanheng Liu
1,2
, Haifeng Zhu
1,2
and Jian Wang
1,2
1
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2
Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,
Jilin University, Changchun 130012, China
Keywords:
Secure Vehicular Communications, Social Networks, Privacy Preservation.
Abstract:
Internet Key Exchange (IKE) can be used in vehicular communications in vehicular ad hoc networks
(VANETs), but it requires a stable communication procedure, which is usually difficult to achieve because
of the highly dynamic context of a VANET. In this paper, we propose a new method named social-network-
based-privacy-preserving broadcast (SNB-PPB) for secure vehicular communications, which can omit the key
exchange procedure. It transfers the information of online social networks to offline vehicular communica-
tions, dividing messages into different privacy levels and vehicles into different trust levels based on trust
relations on social networks. The sender uses attributes on social networks and the message’s privacy level
to encrypt messages; only those receivers who satisfy the corresponding trust level can decrypt the broadcast
packet.To the best of our knowledge, this is the first time that social networks have been applied in VANETs
to achieve secure vehicular communications and privacy preservation. Our simulation results show that our
method enables the sender to decide the amount and percentage of vehicles that can decrypt the received
broadcast.
1 INTRODUCTION
A vehicular ad hoc network (VANET) is a special
form of wireless ad hoc network that allows informa-
tion dissemination between vehicles (V2V) and with
nearby roadside infrastructures (V2I) for facilitating
numerous attractive and exciting applications with the
aim of creating a safer and more efficient traffic envi-
ronment (Xu et al., 2015). It can support vehicular in-
telligent control, such as collision avoidance, as well
as an active safety system by allowing the exchange
of a vehicle’s information, such as speed, location,
and acceleration. However, the information dissem-
inated within the network contains users’ private in-
formation and the communication procedure is at risk
of eavesdropping, masquerading, trace tracking, and
more problems, and therefore, more attention should
be paid to the security of the communication proce-
dure.
Many existing encryption algorithms have been
ported and deployed to secure a range of vehicular
applications, e.g., Internet Key Exchange version 2
(IKEv2) (Alsa’deh et al., 2013). However, simulation
and experimental results show that the algorithms suf-
fer from poor performance, because the key exchange
procedure requires stable communication, which is
difficult to achieve in a VANET (Wang and Li, 2009).
For this purpose, we use social networks and
ciphertext-policy attribute-based encryption (CP-
ABE)(Bethencourt et al., 2007) . Currently, social
networks are becoming increasingly popular. Let us
take Facebook as an example. Every account has a
set of attributes, such as the ID, location, and sex
of the holder. These attributes are sufficient to al-
low an individual to identify him/herself. It also pro-
vides a friends list, most members of which are the
holder’s families, classmates, friends, colleagues, and
so on. In other words, most members are “trusted peo-
ple.” Furthermore, an individual’s “trusted friend’s
friend” is frequently trustworthy but less so than
his/her “friend.” Thus, dissemination of trust exists
among the social network nodes. As can be seen, the
social network contains trust relationships. These at-
tributes and trust relationships can help create the ac-
cess structure and encrypt messages.
In this paper, we propose a method named social-
network-based-privacy-preserving broadcast (SNB-
PPB) for improving the security of information ex-
196
Liu, Y., Zhu, H. and Wang, J.
SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications.
DOI: 10.5220/0006799901960203
In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 196-203
ISBN: 978-989-758-293-6
Copyright
c
 2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

change in vehicular communications, which com-
bines a trust-based privacy-preserving strategy and
CP-ABE. When a vehicle user wishes to send a mes-
sage to certain other vehicles, the vehicle user’s social
network attributes and the message’s privacy level can
be used to create an access structure and encrypt the
message. Only those vehicles that satisfy the corre-
sponding trust level can satisfy the access structure
and decrypt the message packet.
The rest of the paper is structured as follows. Sec-
tion 2 provides definitions used in our method and a
problem statement. It also introduces previous work
on the aspects of communication security and privacy
preserving in VANETs. Section 3 presents related
technologies used in our method. It also specifies the
system model and the algorithms’ definition. We give
the specific simulations and analysis in Section 4. Fi-
nally, we conclude the paper in Section 5.
2 PROBLEM STATEMENT AND
BACKGROUND
2.1 Problem Statement
Usually, a key exchange procedureis performedwhen
one vehicle meets another and would like secure com-
munication with the second vehicle in a VANET. The
key exchange procedure can be considered a means of
distinguishing between authorized and other vehicles.
In social networks, the nodes other than the user’s
node can be divided into different trust levels based
on the relations between them and the user (“me”),
as shown in Fig. 1. This strategy can also be ap-
plied to differentiate different vehicles for vehicular
communications. Every vehicle driver can connect
the vehicle to his/her social network account. Then,
the vehicle has attributes and relations with other ve-
hicles. We can also classify the vehicle’s surrounding
vehicles into different trust levels. Since vehicles be-
yond the user’s vehicle’s communication radius can-
not receive the broadcast, we pay more attention to
the user’s geographical neighbors. If a user sends a
message encrypted with the attributes of his/her so-
cial network account and its privacy level is tl, then
the user’s geographical neighbors whose trust level is
m can decrypt the message iff m ≤ tl. As can be seen,
we can set the message’s privacy level to determine
who is able to decrypt the message.
2.2 Definitions
N-Hops neighbor. Assuming that every social net-
work account is one node, if B is A’s friend in the
d
f
j
a
e
Me
b
c
k
i
h
l
g
7UXVWOHYHORQHRQHKRSVQHLJKERU
7UXVWOHYHOWZRWZRKRSVQHLJKERU
7UXVWOHYHOWKUHHWKUHHKRSVQHLJKERU
b:trust level 1 k:trust level 2
n:trust level 3
o:trust level 3
h:trust level 2
r:trust level 3
f:trust level 1
Me
n
m
r
t
p
o
s
q
Figure 1: Social networks and vehicular communications.
social network, then a line is added between A and B.
Then, we can draw a graph based on the social net-
work. Exactly as in a computer network graph, if the
length of the shortest path from node C to node D in
the social network graph is N, we call D C’s N-Hops
neighbor.
Trust level tl and privacy level pl. tl is used to
describe the level of trust one vehicle deserves. If B
is A’s i-Hops neighbor, then B’s trust level for A is
i. Trust level 1 is the highest trust level. The privacy
level, pl, is used to describe the degree of privacy. It
can determine the number of people who can decrypt
the encrypted message. The value of pl is lower if
fewer people are allowed to decrypt the message.
n and Mhops. This is an attribute set by the sender
when encrypting messages that is used to help realize
the privacy level. If the receiver is in trust level N and
N ≤ n, then the receiver is able to decrypt the mes-
sage. n implies the privacy level of the message. The
higher the privacy level of the message, the smaller is
the value of n. Mhops represents the max value of n.
1 ≤ n ≤ Mhops.
Conditions C and k. C represents the plaintext in
the broadcast, which consists of a set of attributes. It
is used to partially identify the user and reduce the de-
cryption calculation. k ∈ ([0,1]) representsC’s ability
to reduce calculation. The value of k is larger when
the ability is lower.
Communication radius r and geographical neigh-
bor. The broadcast sent by a vehicle can be received
by other vehicles within an area. The range of the
area is determined by the sending power of the sender,
the sensitivity of receiver’s sensor, and the environ-
ment (see (Ingelrest and Simplot-Ryl, 2006)). In our
method, for simplicity we consider the area to be a
circle, the radius of which is r. Then, vehicles located
within the circle are called the sender’s geographical
neighbors.
Decryption percentage p. This represents the per-
centage of all the geographical neighbors of a vehicle
that broadcasts a message that can decrypt the cipher-
text of the encrypted message.
SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications
197

2.3 Related Work
Some progress on vehicular communication has al-
ready been achieved and presented in the literature.
Wen-Bing et al. (Horng et al., 2012) proposed a novel
group communication scheme for vehicular networks,
in which a group is formed by a set of related ve-
hicles having the same destination, such as a group
of recreational vehicles traveling to the same tourist
spot. Choi et al. (Choi and Jung, 2009) proposed a
security framework with strong non-repudiation and
privacy properties using a new approach constituting
an ID-based cryptosystem in VANETs.
Goyal et al. (Goyal et al., 2006) developed a new
cryptosystem for fine-grained sharing of encrypted
data, called key-policy attribute-based encryption, in
which ciphertexts are labeled with sets of attributes
and private keys are associated with access structures
that controlwhich ciphertextsa user is able to decrypt.
Liu et al. (Liu et al., 2016) proposed a communication
model for VANETs by extending CP-ABE (Bethen-
court et al., 2007) with a hierarchical structure of mul-
tiple authorities to achieve fine-grained access control
of the transmitted messages.
In the methods in most previous studies, every
vehicle needs authentication before communication
is allowed. Some methods search for a third party
to record every vehicle serially before communica-
tion; however, these methods are not practical, since
the number of vehicles is very large, and they lack
the required flexibility given that one vehicle may
meet many other vehicles that have not previously
appeared. Some methods are based on IKE, which
suffers problems related to the high mobility and fre-
quent topology changes in VANETs.
In this paper, we propose SNB-PPB to provide
better information exchange security in vehicular
communications. We use the relations between ve-
hicle drivers on social networks to assist the authen-
tication and use social network attributes to facilitate
the encryption procedure. The method omits the key
exchange procedure, which needs stable communica-
tion, and therefore it can be effective in a VANET.
3 MODELS
3.1 Preliminary Knowledge
3.1.1 Trust Evaluation Model
Like human beings, vehicles need to send messages
having varying degrees of importance: some are “very
important,” some are “normally important,” and some
may be “not important.” Correspondingly, it is ex-
pected that “very important” messages will be made
known only to “very trusted vehicles and “normally
important” messages to “very trusted” and “normally
trusted” vehicles. Therefore, we propose a trust eval-
uation model to evaluate the extent to which one ve-
hicle can trust another in a VANET.
In social networks, users havemany “friends” who
deserve trust. Then, these friends’ friends also de-
serve trust; however, an attenuation in trust will ex-
ist. Thus, trust is disseminated among the social net-
work nodes and an attenuation in trust exists during
dissemination. We used this idea to create our trust
evaluation model. In our model, we use trust levels
to describe the concept “trust” and for simplicity con-
sider the trust attenuation during dissemination to be
linear. Each vehicle has a social network account and
when the distance from B to A in a social network
grows by one degree, B’s trust level for A grows by
one degree. When vehicle A is running on the road,
there are many other vehicles nearby, e.g., B. If A is
B’s x-Hops neighbor, B is in trust level x for A. Thus,
all vehicles can be divided into different trust levels
for A.
3.1.2 Ciphertext-policy Attribute-based
Encryption Model
Access structure T. Usually, a tree with root R is used
to represent an access structure. Each non-leaf node
of the tree represents a threshold gate, described by
its children and a threshold value. If num(x) is the
number of children of a node x and k
x
is its threshold
value, then 0 < k
x
≤ num(x). When k
x
= 1, the thresh-
old gate is an OR gate and when k
x
= num(x) it is an
AND gate. Each leaf node x of the tree is described
by an attribute and a threshold value k
x
= 1. The par-
ent of the node x in the tree is denoted by parent(x).
The function att(x) is defined only if x is a leaf node
and denotes the attribute associated with the leaf node
x in the tree. The access tree T also defines an order-
ing between the children of every node; that is, the
children of a node are numbered from 1 to num. The
function index(x) returns such a number associated
with the node x, where the index values are uniquely
assigned to nodes in the access structure for a given
key in an arbitrary manner.
Satisfying the access tree. Let T be an access tree
with root R. Denote by T
x
the subtree of T rooted at
the node x. Then, T is the same as T
R
. If a set of
attributes γ satisfies the access tree T
x
, it is denoted by
T
x
(γ) = 1. T
x
(γ) is computed recursively as follows. If
x is a non-leaf node, T
x
′
(γ) is evaluated for all children
x
′
of node x. T
x
(γ) returns 1 if and only if at least k
x
VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems
198

children return 1. If x is a leaf node, then T
x
(γ) returns
1 if and only if att(x) ∈ γ.
3.2 Our Construction
3.2.1 Setup
The setup algorithm selects a bilinear group G
0
of
prime order p with generator g. Then, it selects two
random exponents α,β ∈ Z
p
. The public key is pub-
lished as
PK = G
0
,g,h = g
β
, f = g
1/β
,e(g,g)
α
. (1)
and the master key MK is (β,g
α
).
3.2.2 Encrypt and Send
The broadcast packet sent by the sender consists of
two parts. The first part is conditions C (see Sec-
tion 2), which describes part of the attributes of
the sender’s social network account in order to re-
duce the calculation when the receiver wants to de-
crypt the packet. The second part is the ciphertext,
which is the result of the encryption. The sender
sets two types of attribute to form the access struc-
ture. The first is a set of attributes S
en
, which can
identify the social network account itself uniquely.
The second is an attribute n. n cannot be exces-
sively large because of the “Six Degrees of Separa-
tion. Having succeeded in the real world, the the-
ory in truth holds for online societies (Zhang and Tu,
2009). Therefore, n ≤ Mhops. In our text, we use the
attributes ID and location to form S
en
. As an exam-
ple, where the ID is windy123456 and the location is
Changchun, we set n = 3 to complete the access tree:
(”windy123456”) AND (”changchun”) AND (”n 1”
OR ”n
2” OR ”n 3”).
The encryption algorithm takes the public key,
PK, the message, M, and the access structure, AC, as
input, and outputs ciphertext, which implicitly con-
tains AC. Then, the ciphertext is broadcast.
The encryption procedure is as follows. The al-
gorithm first selects a polynomial q
x
for each node x
(including the leaves) in the tree T. These polyno-
mials are selected as follows in a top-down manner,
starting from the root node R. For each node x in the
tree, the degree d
x
of the polynomial q
x
is set to be
one less than the threshold value k
x
of that node, that
is, d
x
= k
x
− 1 .
Starting with the root node R the algorithm se-
lects a random s ∈ Z
p
and sets q
R
(0) = s. Then,
it selects d
R
other points of the polynomial q
R
ran-
domly to define it completely. For any other node x, it
sets q
x
(0) = q
parent(x)
(index(x)) and selects d
x
other
points randomly to completely define q
x
.
Let Y be the set of leaf nodes in T. The ciphertext
is then constructed by giving the tree access structure
T and computing
CT = (T,
˜
C = Me(g,g)
αs
,
C = h
s
,∀y ∈ Y : C
y
= g
q
y
(0)
,C
′
y
= H(att(y))
q
y
(0)
) .
(2)
3.2.3 Decryption
The sender’s geographical neighbors can receive the
packet and would like to decrypt it. We provide two
methods to decrypt the packet: a centralized method
and a distributed method.
First, we introduce the centralized decryption
method. The receiver uploads the packet together
with his/her social network account ID to the server
and applies for decryption. The server responds to
the application and attempts to decrypt the ciphertext.
Since a huge calculation is required to traverse all-
hops neighbors, the servercan reduce the search range
by means of the plaintext conditions in the packet.
The decryption algorithm is described in Algorithm
1.
Algorithm 1: Tentative decryption algorithm in the server.
Require:
social network account ID (id); ciphertext in the
broadcast (cip);
plaintext in broadcast conditions C; Mhops; PK;
MK;
1: i = 1;
2: while i <= Mhops do
3: for all id’s i-Hops neighbors that satisfy C do
4: Take S = S
kg
∪ {n i} and MK as input and
output SK by the key generation algorithm;
5: if SK can help decrypt cip with PK success-
fully then
6: Return the result;
7: end if
8: end for
9: i+ +;
10: end while
11: return fail;
In Step 3, we consider that C is satisfied whenC ⊆
S
pi
.
Step 4 comprises the key generation algorithm. S
and MK are used to generate SK. The algorithm first
selects a random r ∈ Z
p
and then random r
j
∈ Z
p
for
each attribute j ∈ S. Then, it computes the key as
SK = (D = g
(α+r)/β
,∀ j ∈ S : D
j
= g
r
· H( j)
r
j
,
D
′
j
= g
r
j
) .
(3)
SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications
199

Step 5 comprises the decryption algorithm. We
specify our decryption procedure as a recursive algo-
rithm. For ease of exposition, we present the simplest
form of the decryption algorithm.
We first define a recursive algorithm
DecryptNode(CT,SK,x) that takes as input a
ciphertext CT, a private key SK, and a node x from T
.
If the node x is a leaf node, then we let i = att(x)
and define as follows. If i ∈ S, then
DecryNode(CT,SK, x) =
e(D
i
,C
x
)
e(D
i
′
,C
x
′
)
= e(g,g)
rq
x
(0)
.
(4)
If i /∈ S, then we define
DecryptNode(CT,SK,x) =⊥. We now consider
the recursive case where x is a non-leaf node. The
algorithm DecryptNode(CT,SK, x) then proceeds
as follows. For all nodes z that are children of x, it
calls DecryptNode(CT,SK,z) and stores the output
as F
z
. Let S
x
be an arbitrary k
x
− sized set of child
nodes z such that F
z
6=⊥. If no such set exists, then
the node was not satisfied and the function returns ⊥.
Otherwise, we compute
F
x
=
∏
z∈S
x
F
△
i,S
′
x
(0)
z
,where i = index(z),
S
′
x
= { index(z) : z ∈ S
x
}
= e(g,g)
r·q
x
(0)
(using polynomial interpolation) .
(5)
Having defined our function DecryptNode, we can
now define the decryption algorithm. The algorithm
begins by simply calling the function on the root node
R of the tree T. If the tree is satisfied by S, A =
DecryptNode(CT,SK,r) = e(g,g)
rq
R
(0)
= e(g,g)
rs
is
set, and the algorithm now decrypts by computing
˜
C/(e(C,D)/A) =
˜
C/(e(h
s
,g
(α+r)/β
)/e(g,g)
rs
) = M .
(6)
Next, we introduce the distributed decryption
method. One difference exists between the central-
ized and the distributed method in the encryptionstep.
The attribute n is not used in encryption, but instead
the plaintext of the packet is used. The decryption
procedure is completely different from that in the cen-
tralized method. The main idea is that when A re-
ceives the packet, he/she tries using every friend’s at-
tributes on the friend list of his/her social network ac-
count to decrypt the packet. If A cannot decrypt the
packet, he/she sends the packet to his/her friends for
decryption (by the cellular network). If one of A’s
friends can decrypt it, the friend sends the result back;
if not, the friend sends the packet to his/her friends’
friends. It is a recursive procedure. The decryption
algorithm is described in Algorithm 2.
Algorithm 2: Recursive decryption algorithm RDA.
Require:
social network account ID (id); ciphertext in the
broadcast packet (cip);
Conditions C; n; SOURCE ID (source
id)); PK;
MK;
1: for all id’s friends that satisfy C do
2: Take S = S
kg
and MK as input and output SK
by the key generation algorithm;
3: if SK can help decrypt cip with PK success-
fully then
4: Return the result to source
id;
5: end if
6: end for
7: if n == 1 then
8: return fail to source
id;
9: else
10: n = n− 1;
11: send the packet to all friends except source
id;
12: end if
13: for all id’s friends do
14: if id obtains the successful result from some
friend then
15: return the result to source
id;
16: end if
17: end for
18: return fail to source
id;
4 SIMULATIONS AND ANALYSIS
We needed to conduct experiments to explore which
factors affect the decryption percent p. Indirectly, we
can infer this by the distribution of the hops from ge-
ographical neighbors to the sender. There are three
groups of social networks from the Internet. Figure 2
depicts the social networks relations of Twitter, Face-
book, and Hamsterster, respectively. There are also
three groups of trajectories. We performed a random
mapping between trajectories and social networks to
form three groups of data. Then, every vehicle had
one trajectory and a social network account in each
group. Since the decryption method type does not af-
fect whether a packet can be decrypted or not, we do
not need to distinguish the centralized and distributed
decryption method in the feasibility analysis.
First, we explored the manner in which the de-
cryption percentage p changes according to the com-
munication radius r. In each group, every vehicle had
VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems
200

Figure 2: Social networks.
Figure 3: Decryption percentage p vs. communication ra-
dius r in Twitter.
Figure 4: Decryption percentage p vs. communication ra-
dius r in Facebook.
a trajectory and a social network account. Then, we
chose one moment and recorded the location of each
vehicle. We changed r and observed the change in
p. Figures. 3, 4, and 5 show that r does not affect
p. The number of the sender’s geographical neigh-
bors changes when r changes. However, p does not
change, since the hops from other vehicles to the
sender are fixed after mapping. We provide a proof
in the following.
Then, we explored the manner in which the de-
cryption percentage p changes according to time t. In
SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications
201

Figure 5: Decryption percentage p vs. communication ra-
dius r in Hamsterster.
Figure 6: Decryption percentage p vs. time t in Twitter.
each group, every vehicle had a trajectory and a social
network account. Then, we set the communication ra-
dius as 300 m. We changed t and observed the change
in p. According to the results shown in Figs. 6, 7, and
8, time also does not affect p. Therefore, we can con-
clude that r and time do not affect p. We provide a
proof.
Given N vehicles and corresponding random map-
ping social network accounts and their trajectories,
assuming vehicles have a random distribution in the
map, vehicle density is ρ(vehicles/m
2
), and commu-
nication radius is r, then r and time t do not affect the
decryption percentage p.
Figure 7: Decryption percentage p vs. time t in Facebook.
Figure 8: Decryption percentage p vs. time t in Hamster-
ster.
Proof. One vehicle A is located in Location; then,
the number of A’s geographical neighbors is m =
h(Location(time),r,ρ), where h() is a function to cal-
culate the number of vehicles and Location changes
according to time. Since the hops distribution
from every other vehicle to A in the social network
is fixed after mapping, we can define it as
~
P =
(p
1
, p
2
...p
n
),
∑
n
i=1
p
i
= 1. All the distribution of other
vehicles is random, and therefore, for these m vehi-
cles, the mathematical expectation of their distribu-
tion number is m·
~
P. The percentage of hops distribu-
tion is m·
~
P/m =
~
P. Therefore, m does not affect hops
distribution. In other words, the time and the commu-
VEHITS 2018 - 4th International Conference on Vehicle Technology and Intelligent Transport Systems
202

nication radius do not affect decryption percentage p
when vehicles are mapped to social network accounts
one to one randomly.
We can conclude that p is related only to the type
of social network in random mapping and the sender
can set a different value of n to achieve a different
decryption percentage in our method. Therefore, the
sender can set n based on the privacy level of the mes-
sage and decide which individuals are trustworthy to
decrypt the message. The decryption percentage can
reach 100%. Therefore, our method is practical.
5 CONCLUSIONS AND FUTURE
WORK
In this paper, we proposed a new method for se-
cure vehicular communications, called SNB-PPB,
that does not require key exchange, in order to han-
dle the frequently changing vehicular context. Our
method uses social networks to facilitate message en-
cryption and decryption. We explored all the factors
that may affect the decryption percentage. We can
concludethat parameters can be set to ensure that only
the vehicles allowed by the sender can decrypt his/her
encrypted messages. Moreover, we offered two types
of decryption mode to suit different potential scenar-
ios. We will do further researches on performance
evaluation and improvement in the future.
ACKNOWLEDGMENT
This paper was partially supported by: National
Nature Science Foundation, Grant/Award Number:
61373123, 61572229 and U1564211; Scientific Re-
search Foundation for Returned Scholars; Interna-
tional Scholar Exchange Fellowship (ISEF) pro-
gram of Korea Foundation for Advanced Stud-
ies (KFAS); Jilin Provincial Science and Technol-
ogy Development Foundation, Grant/Award Num-
ber: 20170204074GX; Jilin Provincial Interna-
tional Cooperation Foundation, Grant/Award Num-
ber: 20150414004GH; Premier-Discipline Enhance-
ment Scheme supported by Zhuhai Government; Pre-
mier Key-Discipline Enhancement Scheme supported
Guangdong Government Funds.
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